Abstract
Background
Conduct disorder (CD) is a common syndrome with far-reaching effects. Risk factors
for the development of CD span social, psychological, and biological domains. Researchers
note that predictive models of CD are limited if the focus is on a single risk factor
or even a single domain. Machine learning methods are optimized for the extraction
of trends across multidomain data but have yet to be implemented in predicting the
development of CD.
Methods
Social (e.g., family, income), psychological (e.g., psychiatric, neuropsychological),
and biological (e.g., resting-state graph metrics) risk factors were measured using
data from the baseline visit of the Adolescent Brain Cognitive Development Study when
youth were 9 to 10 years old (N = 2368). Applying a feed-forward neural network machine learning method, risk factors
were used to predict CD diagnoses 2 years later.
Results
A model with factors that included social, psychological, and biological domains outperformed
models representing factors within any single domain, predicting the presence of a
CD diagnosis with 91.18% accuracy. Within each domain, certain factors stood out in
terms of their relationship to CD (social: lower parental monitoring, more aggression
in the household, lower income; psychological: greater attention-deficit/hyperactivity
disorder and oppositional defiant disorder symptoms, worse crystallized cognition
and card sorting performance; biological: disruptions in the topology of subcortical
and frontoparietal networks).
Conclusions
The development of an accurate, sensitive, and specific predictive model of CD has
the potential to aid in prevention and intervention efforts. Key risk factors for
CD appear best characterized as reflecting unpredictable, impulsive, deprived, and
emotional external and internal contexts.
Keywords
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Article Info
Publication History
Published online: February 22, 2022
Accepted:
February 8,
2022
Received in revised form:
January 10,
2022
Received:
November 18,
2021
Publication stage
In Press Journal Pre-ProofIdentification
Copyright
© 2022 Society of Biological Psychiatry.
